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1
Introduction
2
About Hugging Face
3
What is Transfer Learning
4
Transform Networks
5
Transfer Learning Pipeline
6
Tokenization
7
Words
8
BPE
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Results
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BBPE
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Why tokenization
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Pipeline
13
Code
14
Fornication
15
Normalizer
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Transformer Architecture
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Abstract Classes
18
Model Hub
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Pipeline abstraction
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Sentiment analysis
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Question answering
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Language modeling
23
Sequence classification
24
Defining classes
25
Training Models
Description:
Explore the cutting-edge world of Natural Language Processing (NLP) in this 49-minute talk from Databricks. Dive into the transformative impact of transformer networks since 2017, examining models like BERT, XLNet, ALBERT, and ELECTRA. Learn how to build a comprehensive NLP pipeline using Hugging Face tools, from text tokenization with huggingface/tokenizers to generating predictions with huggingface/transformers. Discover the power of transfer learning, understand the intricacies of tokenization methods like BPE and BBPE, and gain insights into transformer architecture. Get hands-on with code examples for various NLP tasks including sentiment analysis, question answering, language modeling, and sequence classification. Master the art of defining classes, training models, and leveraging the Model Hub for state-of-the-art NLP applications.

Building a Pipeline for State-of-the-Art NLP Using Hugging Face Tools

Databricks
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